Financial frictions can reduce aggregate productivity, in particular when firms with high
productivity cannot borrow against their profits. This paper investigates the quantitative
importance of this form of borrowing constraint using a large panel of firms in Japan. The firms
are young and unlisted, precisely the firms for which credit frictions are expected to be the
most severe. In this data, I find that firm leverage (asset-to-equity ratio) and firm
output-to-capital ratios rise with firm productivity, both over time in a firm and across firms of the same age and cohort. I use these facts in indirect inference to estimate a standard general equilibrium model where financial frictions arise from the limited pledgeability of profits and capital. In this model more financially constrained firms have higher output-to-capital ratios. The model matches the two facts the best when firms can pledge half of their one-year-ahead profits and one-fifth of their assets. Compared to the common assumption that firms can pledge only assets, aggregate productivity loss due to financing frictions is one-third smaller when profits are also pledgeable to the degree seen in Japan.
Across cohorts of firms and plants within the U.S., Indonesia, India and
China, we find that average discounted profits rise systematically with average labor productivity at the time of entry. The number of entrants, in
contrast, is weakly connected to average labor productivity but closely tied
to aggregate employment. In many models of firm dynamics, growth, and
trade, these facts imply that the cost of creating a new business is increasing with average productivity given a zero profit condition for entrants. Entry costs could rise as development proceeds because entry is laborintensive and/or because it is more expensive to set up firms using more skilled workers and more sophisticated technology.
We study the asymptotic distribution of simulation estimators, where the same set of draws are used for all observations under general conditions that do not require the function used in the simulation to be smooth. We consider two cases: estimators that solve a system of equations involving simulated moments and estimators that maximize a simulated likelihood. Many simulation estimators used in empirical work involve both overlapping simulation draws and non-differentiable moment functions. Developing sampling theorems under these two conditions provides an important complement to the existing results in the literature on the asymptotics of simulation estimators.
Published Articles (Refereed Journals and Volumes)
This paper derives explicit error bounds for numerical policies of η-concave stochastic dynamic programming problems, without assuming the optimal policy is interior. We demonstrate the usefulness of our error bound by using it to pinpoint the states at which the borrowing constraint binds in a widely used income fluctuation problem with standard calibrations and a firm production problem with financial constraints.
Solving the Income Fluctuation Problem with Unbounded Rewards
Journal of Economic Dynamics and Control 45, August 2014, 353-365 | With Stachurski
This paper studies the income fluctuation problem without imposing bounds on utility, assets,
income or consumption. We prove that the Coleman operator is a contraction mapping over the
natural class of candidate consumption policies when endowed with a metric that evaluates
consumption differences in terms of marginal utility. We show that this metric is complete,
and that the fixed point of the operator coincides with the unique optimal policy. As a
consequence, even in this unbounded setting, policy function iteration always converges to the
optimal policy at a geometric rate.
Generalized Look-Ahead Methods for Computing Stationary Densities
Mathematics of Operations Research Publication 37 (3), August 2008, 489-500 | With Braun and Stachurski
The look-ahead estimator is used to compute densities associated with Markov processes via simulation. We study a framework that extends the look-ahead estimator to a broader range of applications. We provide a general asymptotic theory for the estimator, where both L_1 consistency and L_2 asymptotic normality are established. The L_2 asymptotic normality implies root-n convergence rates for L_2 deviation.
Slowing growth of total factor productivity has led some to suggest that the world is running out of ideas for innovation. This column suggests that the way output is measured is vital to assessing this, and quantifies the role of imputation in output measurement bias. By differentiating between truly ‘new’ and incumbent products, it finds missing growth in the US economy. Accounting for this missing growth will allow statistical offices to improve their methodology and more readily recognise the ready availability of new ideas, but also has implications for optimal growth and inflation targeting policies.